Design of Highly-Accurate and Hardware-Efficient Spiking Neural Networks
Bibliographic record
Abstract
Spiking neural networks (SNNs) have emerged as a promising alternative to conventional artificial neural networks (ANNs) for energy efficient design. The rate encoded computation in SNNs utilizes a number of spikes in a time window to encode information. In a similar but different scheme, stochastic computing (SC) encodes binary numbers into and operates on random binary bit streams. In this article, we first propose a hardware-efficient design of stochastic SNNs that attains a high accuracy. As a network becomes more complex or the number of neurons increases, memory usage tends to grow exponentially. Inspired by the notion of binarized neural networks (BNNs), we further propose the design of a weight-binarized SNN (WB-SNN) to reduce the stringent requirement in memory usage in SNNs. Both designs take advantage of a priority encoder to transform the spikes between layers of neurons into index-based signals. In this way, it mitigates the issue of requiring significant hardware resources for a relatively low information density. Additionally, a WB-SNN based convolutional neural network (CNN) is designed for the recognition task of larger datasets. An implementation on field programmable gate arrays (FPGAs) for the Modified National Institute of Standards and Technology (MNIST) image recognition dataset shows that the stochastic SNN design achieves a higher accuracy with smaller hardware compared to other SNNs. Validated by using a multi-layer-perceptron and a CNN on the MNIST and CIFAR-10 datasets, respectively, the WB-SNN achieves a significant saving in memory with only a limited accuracy loss compared with its SNN and BNN counterparts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".